> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Knowledge Bases

> A knowledge base is a centralized repository for information. Knowledge bases allow you to store, organize, and provide access to information (ingest and query on your data).

## What is a knowledge base?

A knowledge base consists of a single [vector store](https://scale-egp.readme.io/docs/vector-stores-intro) along with several [data connectors](https://scale-egp.readme.io/docs/data-connectors) that point to different external data sources. As shown in the diagram below, knowledge bases are a fundamental component of the SGP API ecosystem as you need a knowledge base to ingest data through a data connector.

<Frame>
  <img src="https://mintcdn.com/scalegp/1vQnbXFkjbQbcgd1/images/1c4534e-Screenshot_2023-07-28_at_3.23.30_PM.png?fit=max&auto=format&n=1vQnbXFkjbQbcgd1&q=85&s=f91124a5989498a92970e8b869c01ae5" width="3406" height="1268" data-path="images/1c4534e-Screenshot_2023-07-28_at_3.23.30_PM.png" />
</Frame>

**How are knowledge bases related to vector stores?** Every knowledge base has one [vector store](https://scale-egp.readme.io/docs/vector-stores-intro) under the hood. Any data that is imported or ingested into a knowledge base will be embedded and stored in its vector store.

**How are knowledge bases related to data connectors?** When you create a data connector, you associated it with a single knowledge base. Any data that is ingested via that data connector will be stored in its associated knowledge base.

## Creating a new knowledge base in the UI

To create a new knowledge base, you need to:

1. Define a Knowledge Base Name.
2. Select an Embedding Model.
3. Select a Data Source.
4. Configure Upload Source.

### Knowledge Base Name

This will be used to locate your knowledge base after you create it.

### Embedding Model

Embedding models are ML models that convert text into a numerical representation. The representation is then used to compare and match text data. You can select an existing model on the SGP Platform or install a new model and use the new model.

### Data Source

Data sources provide the data for knowledge bases to ingest. When a knowledge base upload is created from a data source, it will read data from it, extract text from relevant files, split it into chunks, embed the chunks, and store the embeddings in a vector database for future retrieval.

### Configure Upload

Upload configurations define how the data source is split into chunks. The chunking configuration affects the relevance of the content retrieved from a knowledge base when the LLM is used to embed content. You can choose to go with the default configuration or choose your own Chunk Strategy, Size, Overlap, and Separator.

## Knowledge Base API

The starter code below creates an SGP knowledge base using the [Create Knowledge Base](/reference/knowledge-bases/create-knowledge-base) endpoint. Fill in your API key and choose a name for your knowledge base, and try out the starter code below!

```Python Python theme={null}

import requests

# Replace this with your SGP API key
# See instructions for getting your API key here: scale-egp.readme.io/docs/getting-started
API_KEY = '[Your API key]'

# Choose a name for your knowledge base
KNOWLEDGE_BASE_NAME = "my_knowledge_base_1"
# Select an embedding model of the two options listed below
EMBEDDING_MODEL = "openai/text-embedding-ada-002"  # or "sentence-transformers/all-MiniLM-L12-v2"

print(f"Creating a knowledge base named {KNOWLEDGE_BASE_NAME}...")
url = "https://api.sgp.scale.com/v3/knowledge-bases"

payload = {
    "embedding_config": {
        "type": "base",
        "embedding_model": "openai/text-embedding-3-large"
    },
    "knowledge_base_name": "myknowledge_base"
}

headers = {
    "accept": "application/json",
	  "content-type": "application/json",
    "x-api-key": API_KEY
}
response = requests.post(url, json=payload, headers=headers)
print(f"Response: {response.text}")
```

You should see a response like the following, which will show your knowledge base ID.

`{"knowledge_base_id":"clk123456789"}`

Keep this value—you will need it later when getting information about your knowledge base, creating data connectors, and more.